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library(tidyverse)
# install for visualizations
library(ggplot2)
# install to combine date and time
library(lubridate)
wego <- read_csv("../data/Route 50 Timepoint and Headway Data, 1-1-2023 through 5-12-2025.csv")
wego
# Create new date time column
wego$DATE_TIME <- ymd(wego$DATE) + hms(wego$SCHEDULED_TIME)
# Examine Data
wego
# Filter February TSP values
feb3_10_tsp <- wego |>
filter(between(DATE_TIME,
as.Date("2025-02-03 12:00:00"),
as.Date("2025-02-10 12:00:00")))
# Filter Feb-Apr TSP with busses only 2 minutes late or more
feb10_apr28_tsp <- wego |>
filter(between(DATE_TIME,
as.Date("2025-02-10 12:00:00"),
as.Date("2025-04-28 12:00:00"))) |>
filter(ADHERENCE <= -2)
# Filter May TSP values
may5_12_tsp <- wego |>
filter(between(DATE_TIME,
as.Date("2025-05-05 12:00:00"),
as.Date("2025-05-12 12:00:00")))
# Add day of week column
wego <- wego |>
mutate(
DATE_TIME = as.POSIXct(DATE_TIME),
DAY_OF_WEEK = wday(DATE_TIME,
label = TRUE,
abbr = FALSE))
wego
NA
# Combine tsp variables into one
tsp_rows <- bind_rows(
feb3_10_tsp,
feb10_apr28_tsp,
may5_12_tsp
) |>
select('ADHERENCE_ID', 'DATE_TIME') |>
distinct() |>
mutate(tsp = 1) # Add tsp indicator column for each distinct adherence id
wego <- wego |>
left_join(
tsp_rows,
by = c('ADHERENCE_ID', 'DATE_TIME')
) |>
mutate(tsp = coalesce(tsp, 0))
wego |> view()
wego <- wego |> mutate(
tsp_indicator = if_else(
between(DATE_TIME,
as.Date("2025-02-03 12:00:00"),
as.Date("2025-02-10 12:00:00")) |
(between(DATE_TIME,
as.Date("2025-02-10 12:00:00"),
as.Date("2025-04-28 12:00:00")) &
ADHERENCE <= -2) |
between(DATE_TIME,
as.Date("2025-05-05 12:00:00"),
as.Date("2025-05-12 12:00:00")), 1, 0)
)
wego
NA
wego <- wego |> mutate(
HOUR = hms(SCHEDULED_TIME) |>
hour()
)
wego <- wego |>
mutate(
time_of_day = case_when(
between(HOUR, 4, 5) ~ "early_morning",
between(HOUR, 6, 8) ~ "morning_peak",
between(HOUR, 9, 14) ~ "midday",
between(HOUR, 15, 17) ~ "pm_peak",
between(HOUR, 18, 20) ~ "evening",
between(HOUR, 21, 23) ~ "late_night",
between(HOUR, 0, 3) ~ "late_night",
.default = "other"
)
)
wego
NA
tod_table = table(wego$time_of_day)
pt_tod_table <- prop.table(tod_table)
pt_tod_table
early_morning evening late_night midday morning_peak other pm_peak
0.03272224 0.12055936 0.09740419 0.36547452 0.16413139 0.03097748 0.18873082
tod_table
early_morning evening late_night midday morning_peak other pm_peak
20255 74626 60293 226228 101597 19175 116824
barplot(table(wego$time_of_day), main = "Time of day distribution")

table_tod <- pt_tod_table
# Create a color vector
color <- rainbow(nrow(table_tod))
# Set the rotation for x-axis labels to 45 degrees
par(las=2)
# Create the vertically stacked bar plot
bp <- barplot(table_tod, main = "Time of day distribution", col = color)
# Add the legend
legend("topright", legend = rownames(table_tod),cex = 0.75, fill = color)

# Add x-axis labels with a 45 degree angle
# axis(1, at=bp, labels=colnames(table_tod), las=2, cex.axis=2)
late_tod <- table(wego$time_of_day, wego$ADJUSTED_LATE_COUNT)
# Create a color vector
color <- rainbow(nrow(late_tod))
# Set the rotation for x-axis labels to 45 degrees
par(las=2)
# Create the vertically stacked bar plot
bp <- barplot(late_tod, main = "Late bus dist", col = color)
# Add the legend
legend("topright", legend = rownames(late_tod),cex = 0.9, fill = color)
# Add x-axis labels with a 45 degree angle
axis(1, at=bp, labels=colnames(late_tod), las=2, cex.axis=1)

count_tod <- wego |>
count(time_of_day)
count_tod
unique(wego$ADJUSTED_LATE_COUNT)
[1] 0 1
wego
# count_tod <- wego |>
# count(time_of_day)
# # value <- count_tod$n
# value = count_tod
# # table(wego$time_of_day)
# condition <- tod <- c("early_morning", "morning_peak", "midday", "pm_peak", "evening", "late_night", "late_night", "other") #wego$time_of_day
# specie <- wego$ADJUSTED_LATE_COUNT
#
# ggplot(wego, aes(fill=condition, y=value, x=specie)) +
# geom_bar(position="fill", stat="identity")
wego_tod_count_late <- wego |>
group_by(time_of_day, ADJUSTED_LATE_COUNT) |>
summarize(n = n())
wego_tod_count_late
ggplot(wego_tod_count_late, aes(fill=time_of_day, y=n, x=factor(ADJUSTED_LATE_COUNT))) +
geom_bar(position="fill", stat="identity")+
xlab("Ontime (0) and Late (1) Buses") +
ylab("Proportion of Buses") +
ggtitle("Proportion of Late and Ontime Buses")

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